"""
gradio_web_server.py

Entry point for all VLM-Evaluation interactive demos; specify model and get a gradio UI where you can chat with it!

This file is copied from the script used to define the gradio web server in the LLaVa codebase:
https://github.com/haotian-liu/LLaVA/blob/main/llava/serve/gradio_web_server.py with only very minor
modifications.
"""

import argparse
import datetime
import hashlib
import json
import os
import time

import gradio as gr
import requests
from llava.constants import LOGDIR
from llava.conversation import conv_templates, default_conversation
from llava.utils import build_logger, moderation_msg, server_error_msg, violates_moderation

from vlm_eval.serve import INTERACTION_MODES_MAP, MODEL_ID_TO_NAME

logger = build_logger("gradio_web_server", "gradio_web_server.log")

headers = {"User-Agent": "PrismaticVLMs Client"}

no_change_btn = gr.Button.update()
enable_btn = gr.Button.update(interactive=True)
disable_btn = gr.Button.update(interactive=False)


def get_conv_log_filename():
    t = datetime.datetime.now()
    name = os.path.join(LOGDIR, f"{t.year}-{t.month:02d}-{t.day:02d}-conv.json")
    return name


def get_model_list():
    ret = requests.post(args.controller_url + "/refresh_all_workers")
    assert ret.status_code == 200
    ret = requests.post(args.controller_url + "/list_models")
    models = ret.json()["models"]
    models = sorted(
        models, key=lambda x: list(MODEL_ID_TO_NAME.values()).index(x) if x in MODEL_ID_TO_NAME.values() else len(models)
    )
    logger.info(f"Models: {models}")
    return models


get_window_url_params = """
function() {
    const params = new URLSearchParams(window.location.search);
    url_params = Object.fromEntries(params);
    console.log(url_params);
    return url_params;
    }
"""


def load_demo(url_params, request: gr.Request):
    logger.info(f"load_demo. ip: {request.client.host}. params: {url_params}")

    dropdown_update = gr.Dropdown.update(visible=True)
    if "model" in url_params:
        model = url_params["model"]
        if model in models:
            dropdown_update = gr.Dropdown.update(value=model, visible=True)

    state = default_conversation.copy()
    return state, dropdown_update


def load_demo_refresh_model_list(request: gr.Request):
    logger.info(f"load_demo. ip: {request.client.host}")
    models = get_model_list()
    state = default_conversation.copy()
    dropdown_update = gr.Dropdown.update(choices=models, value=models[0] if len(models) > 0 else "")
    return state, dropdown_update


def vote_last_response(state, vote_type, model_selector, request: gr.Request):
    with open(get_conv_log_filename(), "a") as fout:
        data = {
            "tstamp": round(time.time(), 4),
            "type": vote_type,
            "model": model_selector,
            "state": state.dict(),
            "ip": request.client.host,
        }
        fout.write(json.dumps(data) + "\n")


def regenerate(state, image_process_mode, request: gr.Request):
    logger.info(f"regenerate. ip: {request.client.host}")
    state.messages[-1][-1] = None
    prev_human_msg = state.messages[-2]
    if type(prev_human_msg[1]) in (tuple, list):
        prev_human_msg[1] = (*prev_human_msg[1][:2], image_process_mode)
    state.skip_next = False
    return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5


def clear_history(request: gr.Request):
    logger.info(f"clear_history. ip: {request.client.host}")
    state = default_conversation.copy()
    return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5


def add_text(state, text, image, image_process_mode, request: gr.Request):
    logger.info(f"add_text. ip: {request.client.host}. len: {len(text)}")
    if len(text) <= 0 and image is None:
        state.skip_next = True
        return (state, state.to_gradio_chatbot(), "", None) + (no_change_btn,) * 5
    if args.moderate:
        flagged = violates_moderation(text)
        if flagged:
            state.skip_next = True
            return (state, state.to_gradio_chatbot(), moderation_msg, None) + (no_change_btn,) * 5

    text = text[:1536]  # Hard cut-off
    if image is not None:
        text = text[:1200]  # Hard cut-off for images
        if "<image>" not in text:
            # text = '<Image><image></Image>' + text
            text = text + "\n<image>"
        text = (text, image, image_process_mode)
        if len(state.get_images(return_pil=True)) > 0:
            state = default_conversation.copy()
    state.append_message(state.roles[0], text)
    state.append_message(state.roles[1], None)
    state.skip_next = False
    return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5


def http_bot(state, model_selector, interaction_mode, temperature, max_new_tokens, request: gr.Request):
    logger.info(f"http_bot. ip: {request.client.host}")
    start_tstamp = time.time()
    model_name = model_selector

    if state.skip_next:
        # This generate call is skipped due to invalid inputs
        yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 5
        return

    if len(state.messages) == state.offset + 2:
        # First round of conversation
        # (Note): Hardcoding llava_v1 conv template for now
        new_state = conv_templates["llava_v1"].copy()
        new_state.append_message(new_state.roles[0], state.messages[-2][1])
        new_state.append_message(new_state.roles[1], None)
        state = new_state

    # Query worker address
    controller_url = args.controller_url
    ret = requests.post(controller_url + "/get_worker_address", json={"model": model_name})
    worker_addr = ret.json()["address"]
    logger.info(f"model_name: {model_name}, worker_addr: {worker_addr}")

    # No available worker
    if worker_addr == "":
        state.messages[-1][-1] = server_error_msg
        yield (state, state.to_gradio_chatbot(), disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
        return

    # Construct prompt
    prompt = state.get_prompt()

    all_images = state.get_images(return_pil=True)
    all_image_hash = [hashlib.md5(image.tobytes()).hexdigest() for image in all_images]
    for image, im_hash in zip(all_images, all_image_hash):
        t = datetime.datetime.now()
        filename = os.path.join(LOGDIR, "serve_images", f"{t.year}-{t.month:02d}-{t.day:02d}", f"{im_hash}.jpg")
        if not os.path.isfile(filename):
            os.makedirs(os.path.dirname(filename), exist_ok=True)
            image.save(filename)

    # Make requests
    pload = {
        "model": model_name,
        "prompt": prompt,
        "interaction_mode": interaction_mode,
        "temperature": float(temperature),
        "max_new_tokens": int(max_new_tokens),
        "images": f"List of {len(state.get_images())} images: {all_image_hash}",
    }
    logger.info(f"==== request ====\n{pload}")

    pload["images"] = state.get_images()

    state.messages[-1][-1] = "▌"
    yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5

    try:
        # Stream output
        response = requests.post(
            worker_addr + "/worker_generate_stream", headers=headers, json=pload, stream=True, timeout=10
        )
        for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
            if chunk:
                data = json.loads(chunk.decode())
                if data["error_code"] == 0:
                    output = data["text"][len(prompt) :].strip()
                    state.messages[-1][-1] = output + "▌"
                    yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5
                else:
                    output = data["text"] + f" (error_code: {data['error_code']})"
                    state.messages[-1][-1] = output
                    yield (state, state.to_gradio_chatbot()) + (
                        disable_btn,
                        disable_btn,
                        disable_btn,
                        enable_btn,
                        enable_btn,
                    )
                    return
                time.sleep(0.03)
    except requests.exceptions.RequestException:
        state.messages[-1][-1] = server_error_msg
        yield (state, state.to_gradio_chatbot()) + (disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
        return

    state.messages[-1][-1] = state.messages[-1][-1][:-1]
    yield (state, state.to_gradio_chatbot()) + (enable_btn,) * 5

    finish_tstamp = time.time()
    logger.info(f"{output}")

    with open(get_conv_log_filename(), "a") as fout:
        data = {
            "tstamp": round(finish_tstamp, 4),
            "type": "chat",
            "model": model_name,
            "start": round(start_tstamp, 4),
            "finish": round(finish_tstamp, 4),
            "state": state.dict(),
            "images": all_image_hash,
            "ip": request.client.host,
        }
        fout.write(json.dumps(data) + "\n")


title_markdown = """
# Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models
[[Training Code](github.com/TRI-ML/prismatic-vlms)]
[[Evaluation Code](github.com/TRI-ML/vlm-evaluation)]
| 📚 [[Paper](https://arxiv.org/abs/2402.07865)]
"""

tos_markdown = """
### Terms of use
By using this service, users are required to agree to the following terms:
The service is a research preview intended for non-commercial use only. It only provides limited safety measures and may
generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes. For an optimal experience,
please use desktop computers for this demo, as mobile devices may compromise its quality. This Gradio application was built off of the Apache-licensed 
Gradio code released by the LLaVa authors, with light modifications.
"""


learn_more_markdown = """
### License
The service is a research preview intended for non-commercial use only, subject to the model
[License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA,
[Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI,
and [Privacy Practices]
(https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb)
of ShareGPT. Please contact us if you find any potential violation.
"""

block_css = """

#buttons button {
    min-width: min(120px,100%);
}

"""


def build_demo(embed_mode):
    textbox = gr.Textbox(show_label=False, placeholder="Enter text and press ENTER", container=False)

    with gr.Blocks(theme=gr.themes.Default(primary_hue="red", secondary_hue="stone")) as demo:
        state = gr.State()

        if not embed_mode:
            gr.Markdown(title_markdown)

        with gr.Row():
            with gr.Column(scale=3):
                with gr.Row(elem_id="model_selector_row"):
                    model_selector = gr.Dropdown(
                        choices=models,
                        value=models[0] if len(models) > 0 else "",
                        interactive=True,
                        show_label=False,
                        container=False,
                    )

                imagebox = gr.Image(type="pil")
                image_process_mode = gr.Radio(
                    ["Crop", "Resize", "Pad", "Default"],
                    value="Default",
                    label="Preprocess for non-square image",
                    visible=False,
                )

                cur_dir = os.path.dirname(os.path.abspath(__file__))
                gr.Examples(
                    examples=[
                        [f"{cur_dir}/examples/cows_in_pasture.png", "How many cows are in this image?"],
                        [
                            f"{cur_dir}/examples/monkey_knives.png",
                            "What is happening in this image?",
                        ],
                    ],
                    inputs=[imagebox, textbox],
                )

                with gr.Accordion("Parameters", open=False):
                    temperature = gr.Slider(
                        minimum=0.0,
                        maximum=1.0,
                        value=0.2,
                        step=0.1,
                        interactive=True,
                        label="Temperature",
                    )
                    max_output_tokens = gr.Slider(
                        minimum=0,
                        maximum=4096,
                        value=2048,
                        step=64,
                        interactive=True,
                        label="Max output tokens",
                    )

                with gr.Accordion("Interaction Mode", open=False):
                    interaction_modes = list(INTERACTION_MODES_MAP.keys())
                    interaction_mode = gr.Dropdown(
                        choices=interaction_modes,
                        value=interaction_modes[0] if len(interaction_modes) > 0 else "Chat",
                        interactive=True,
                        show_label=False,
                        container=False,
                    )

            with gr.Column(scale=8):
                chatbot = gr.Chatbot(elem_id="chatbot", label="PrismaticVLMs Chatbot", height=550)
                with gr.Row():
                    with gr.Column(scale=8):
                        textbox.render()
                    with gr.Column(scale=1, min_width=50):
                        submit_btn = gr.Button(value="Generate", variant="primary")
                with gr.Row(elem_id="buttons"):
                    # upvote_btn = gr.Button(value="👍  Upvote", interactive=False)
                    # downvote_btn = gr.Button(value="👎  Downvote", interactive=False)
                    # flag_btn = gr.Button(value="⚠️  Flag", interactive=False)
                    # stop_btn = gr.Button(value="⏹️  Stop Generation", interactive=False)
                    regenerate_btn = gr.Button(value="🔄  Regenerate", interactive=False)
                    clear_btn = gr.Button(value="🗑️  Clear", interactive=False)

        if not embed_mode:
            gr.Markdown(tos_markdown)
            gr.Markdown(learn_more_markdown)
        url_params = gr.JSON(visible=False)

        # Register listeners
        btn_list = [regenerate_btn, clear_btn]

        regenerate_btn.click(
            regenerate, [state, image_process_mode], [state, chatbot, textbox, imagebox, *btn_list], queue=False
        ).then(
            http_bot,
            [state, model_selector, interaction_mode, temperature, max_output_tokens],
            [state, chatbot, *btn_list],
        )

        clear_btn.click(clear_history, None, [state, chatbot, textbox, imagebox, *btn_list], queue=False)

        textbox.submit(
            add_text,
            [state, textbox, imagebox, image_process_mode],
            [state, chatbot, textbox, imagebox, *btn_list],
            queue=False,
        ).then(
            http_bot,
            [state, model_selector, interaction_mode, temperature, max_output_tokens],
            [state, chatbot, *btn_list],
        )

        submit_btn.click(
            add_text,
            [state, textbox, imagebox, image_process_mode],
            [state, chatbot, textbox, imagebox, *btn_list],
            queue=False,
        ).then(
            http_bot,
            [state, model_selector, interaction_mode, temperature, max_output_tokens],
            [state, chatbot, *btn_list],
        )

        if args.model_list_mode == "once":
            demo.load(load_demo, [url_params], [state, model_selector], _js=get_window_url_params, queue=False)
        elif args.model_list_mode == "reload":
            demo.load(load_demo_refresh_model_list, None, [state, model_selector], queue=False)
        else:
            raise ValueError(f"Unknown model list mode: {args.model_list_mode}")

    return demo


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--host", type=str, default="0.0.0.0")
    parser.add_argument("--port", type=int)
    parser.add_argument("--controller-url", type=str, default="http://localhost:21001")
    parser.add_argument("--concurrency-count", type=int, default=10)
    parser.add_argument("--model-list-mode", type=str, default="once", choices=["once", "reload"])
    parser.add_argument("--share", action="store_true")
    parser.add_argument("--moderate", action="store_true")
    parser.add_argument("--embed", action="store_true")
    args = parser.parse_args()
    logger.info(f"args: {args}")

    models = get_model_list()

    logger.info(args)
    demo = build_demo(args.embed)
    demo.queue(concurrency_count=args.concurrency_count, api_open=False).launch(
        server_name=args.host, server_port=args.port, share=args.share
    )
